蛋白質在生物體內的功能性表現取決於其三級結構。在過去數十年中，大量的研究投入在蛋白質的相關領域。然而，蛋白質的摺疊問題仍未能被解決，這個問題的挑戰便在於如何透過蛋白質的一級序列資訊來預測一蛋白質的三級結構的三維座標。在本論文中，我們提出一個綜合性的方法，結合了同源模擬法與折疊辨識法，在得知蛋白質一級序列的資訊下，來預測蛋白質的三維座標。在過去的研究中，蛋白質的摺疊問題常透過親疏水性模型來進行模擬，但並未能表現出實際上蛋白質的結構。我們提出一個更精密的切割晶格模型來模擬蛋白質的結構，而除了親疏水性的特性外，我們更加入了在親疏水性模型中所被忽略的雙硫鍵鍵結因素。整個蛋白質的摺疊問題，在我們的方法中，是透過螞蟻系統來進行模擬。根據我的實驗所得到的結果，在透過與真實結構比較的RMSD值來看，我們的方法的確提供了較準確的預測結果。

Functional expression of a protein in life form is decided by its tertiary structure. In the past few decades, a significant number of studies have been made on this subject. However, the folding rules of a protein still stay unsolved. The challenge is to predict the three-dimensional tertiary structure of a protein from its primary amino acid sequence. We propose a hybrid method combining homology model and the folding approach to predict protein three-dimensional structure from amino acid sequence. The previous researches on folding problem mostly take the HP (Hydrophobic-Polar) model, which is not able to simulate the native structure of proteins. We use a more exquisite model, the sliced lattice model, to approximate the native forms. Another essential factor influencing protein structures is disulfide bonds, which are ignored in the HP model. We use the ant colony optimization algorithm to approximate the folding problem with the constrained disulfide bond on the sliced lattice HP model. We show that the prediction results are better than previous methods by the measurement of RMSD(Root Mean Square Deviation).

TABLE OF CONTENTS
Page
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0
Chapter 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Chapter 2. Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1 Properties of Proteins . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.1 Amino Acids in Proteins . . . . . . . . . . . . . . . . . . . . . 6
2.1.2 Levels of protein structures . . . . . . . . . . . . . . . . . . . 6
2.1.3 E®ective Factors in Protein Structures . . . . . . . . . . . . . 10
2.2 Conformational Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3 Structure Prediction Methods . . . . . . . . . . . . . . . . . . . . . . 13
2.4 Sequence Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.5 The Folding Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.6 The Evolutionary Algorithms . . . . . . . . . . . . . . . . . . . . . . 24
2.6.1 The Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . 24
2.6.2 The Ant Colony Optimization Algorithm for the Folding Prob-
lem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.7 The Previous Prediction Method Based on Curve Alignment . . . . . 31
Chapter 3. Our Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Chapter 4. Experimental Results . . . . . . . . . . . . . . . . . . . . . . 63
Page
Chapter 5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

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